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All Journal PROSIDING SEMINAR NASIONAL Jupiter JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal DISPROTEK Bulletin of Computer Science Research Jurnal Teknologi Informasi Cyberku
Akhmad Khanif Zyen
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Multiclass Sentiment Analysis of Electric Vehicle Incentive Policies Using IndoBERT and DeBERTa Algorithms Muhammad Bayu Nugroho; Akhmad Khanif Zyen; Nur Aeni Widiastuti
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9511

Abstract

The electric vehicle (EV) incentive policy in Indonesia has generated various public reactions, particularly on social media platforms. This study aims to classify public sentiment using the IndoBERT and DeBERTa transformer models. A total of 6,758 comments were collected from YouTube, filtered, preprocessed, and labeled into three sentiment categories: positive, negative, and neutral. From this, 1,711 clean data points were used and analyzed in two phases: before and after applying the Random Oversampling technique to address class imbalance. Model performance was evaluated using accuracy, precision, recall, F1-score, and training time. In the initial phase, IndoBERT achieved 96% accuracy with 603.71 seconds of training time, while DeBERTa reached 93% in 439.19 seconds. After balancing and applying 5-Fold Cross Validation, IndoBERT maintained 96% accuracy with balanced metric distribution, while DeBERTa recorded 93% accuracy. IndoBERT performed better in recognizing neutral sentiment, whereas DeBERTa was more time-efficient. These results highlight the effectiveness of local transformer models and data balancing techniques in improving sentiment classification performance on imbalanced datasets.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Syukur Catur Supriyanto Lukman Hakim Muhammad Bayu Nugroho Mulyo, Harminto Nor Safik Nur Aeni Widiastuti Nur Aeni Widiastuti R Hadapiningradja Kusumodestoni Sarwido Zainul Arifin MA